An enduring struggle of SER models is their ability to generalize over languages. This is addressed by using supervision when the languages are supported with sufficient resources, both in data and labels. However, supervised learning is inapplicable to languages beyond the commonly spoken ones, due to insufficient resources in terms of small data sets and few-to-no labels. Therefore, to cater to low-resource languages, it is necessary to develop methods that generalize better over languages. The associated challenge is the subjectivity in emotion perception and classification. For instance, the perception of speech signals demonstrating a neutral emotion can vary between languages, owing to phonetic and cultural differences. Despite this, most models use class labels to train models, instead of employing a more universal and continuous metric like activation and valence from the dimensional model of emotions [1]}.
